This invention relates generally to computed tomography (CT) and more particularly the invention relates to processing of CT image data to reduce adverse noise effects in data acquired using a low x-ray dose.
Cross-sectional images of a patient using x-ray computed tomography have long been employed in medical practice. Briefly, computed tomography is the reconstruction by computer of a tomographic slice or a three-dimensional (3D) image volume of an object or patient. It is generated from multiple x-ray absorption measurements in a scan made around the object's periphery. These projections can be obtained using, for example, a conventional CT scanner with an x-ray source and a detector rotating at a relatively high speed, or with a source and a detector mounted on a C-arm that rotates more slowly around the patient. The fidelity of the image depends upon the nature of the x-ray source and the detectors, the number and speed of the measurements made, and details of the reconstruction algorithm.
An x-ray detector detects a beam of x-rays passing through the body which are attenuated by absorption and by scattering. The amount of absorption depends on the physical density, the atomic composition and the photon energy spectrum of the x-ray beam. For equivalent x-ray energy, a more dense material will attenuate the beam more than a less dense material. X-ray detectors with multiple rows of detector elements, or even a full two-dimensional (2D) matrix, will generate a 2D projection image at every rotation angle. Based on these obtained projections, a reconstruction algorithm computes an attenuation coefficient for each volume element or voxel in the slice.
With the acquisition of an increasing number of projections, it is essential to minimize the radiation dose used. Adaptive anisotropic filtering has the ability to reduce the noise level in low dose data without introducing noticeable blurring.
Three dimensional adaptive filtering as applied to magnetic resonance angiography subsequent to image reconstruction is described by Westin et al. in Journal of Magnetic Resonance Imaging 14: 63-7231 (2001). As described by Westin et al., multi-dimensional adaptive filtering is used as a technique for enhancement of images, image volumes, and volume sequences having temporal resolution. The multi-dimensional adaptive filtering method employs local orientation of structures within the image, such as lines, edges, and planes, to control a set of anisotropic filters. The method is divided into three main steps. The first step includes an estimation of the local orientation of every neighborhood in the original image by assuming that the local orientation can be described locally by a combination of simple features such as lines and planes. In a second step the orientation estimate is stabilized through low pass filtering. Finally, the orientation information is used to control the filtering of the original data in an adaptive fashion.
Li et al. U.S. Patent Publication No. US2006/0062485A1 and Spies et al. International Publication No. WO2005/091219A1 describe the use of filtering to enhance CT images including the use of processing subsequent to image reconstruction to reduce noise effects. However, post processing approaches will be less efficient in reducing for example noise induced streak artifacts compared to processing prior to image reconstruction. It is known to filter CT image data prior to image reconstruction, but it is not believed that adaptive anisotropic filtering utilizing filters that change their shape according to the input data, have been employed.